Building Assistive Communities: The Potential of Liberating Structures for In-Class Peer Mentorship
Bibliographic record
Abstract
Peer mentorship programs have mostly emphasized formal structures, wherein a more experienced student guides a less experienced student. However, these practices are hierarchical and require substantive resources to organize and implement. Searching for alternatives, we research the effectiveness of an informal teaching technique that facilitates active learning and peer-mentorship from everyday classroom settings and processes. Drawing on formative feedback from students enrolled in a lower-level Sociology course over a term, this paper analyzes how a “Liberating Structures” (LS) technique called Five Whys (an adaptation of the Nine Whys of LS) can promote in-class collaboration, peer mentorship, and increased engagement without training and the need to design a formal peer-mentorship program. Students identified many benefits, including that Five Whys promoted community, reflective learning, and deepened engagement with course content. However, the structuring of interactions was seen to be stifling to natural group processes. Broader implications for LS and in-class mentorship are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".